Sucesso_é_esforço_ou_sorte_.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Sucesso é esforço ou sorte?",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XKvY8O6uigo2"
      },
      "source": [
        "# Sucesso é resultado de esforço ou sorte?\n",
        "\n",
        "Esse notebook se baseia no vídeo do Veritassium para mostrar que numa distribuição normal de pessoas candidatas a uma prova, o fator sorte tem um papel fundamental em dizer quem será selecionado/a. Não basta esforço, precisamos de sorte. Isso não tira o valor do esforço, mas serve para nos conscientizar da importância de entender que seleções do genero dificilmente serão puramente por mérito, ainda mais se contar desigualdades que não fatorei nesse notebook"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pcrKQGQtfwhM",
        "outputId": "d24a87f8-acf8-458c-951f-d02774135dd3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 157
        }
      },
      "source": [
        "!pip install tabula-py"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: tabula-py in /usr/local/lib/python3.6/dist-packages (2.2.0)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from tabula-py) (1.18.5)\n",
            "Requirement already satisfied: pandas>=0.25.3 in /usr/local/lib/python3.6/dist-packages (from tabula-py) (1.1.3)\n",
            "Requirement already satisfied: distro in /usr/local/lib/python3.6/dist-packages (from tabula-py) (1.5.0)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.6/dist-packages (from pandas>=0.25.3->tabula-py) (2.8.1)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.6/dist-packages (from pandas>=0.25.3->tabula-py) (2018.9)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.6/dist-packages (from python-dateutil>=2.7.3->pandas>=0.25.3->tabula-py) (1.15.0)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w844pepzipVl"
      },
      "source": [
        "import pandas as pd"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B5s1XrRoirNc",
        "outputId": "bcf63c5d-232e-4cc3-c8fc-60fbcb69886d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "import tabula\n",
        "\n",
        "dados = tabula.read_pdf(\"https://acervo.fuvest.br/fuvest/2020/inscritos_2020.pdf\",\n",
        "                        pages = \"all\")\n",
        "len(dados)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 96
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Tm1DbDd0i3F7",
        "outputId": "29db6e56-f1c2-472c-dcaa-c0868c46bdf8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 288
        }
      },
      "source": [
        "dados[0].head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>CODIGO</th>\n",
              "      <th>NOME</th>\n",
              "      <th>VAGAS</th>\n",
              "      <th>INSCRITOS</th>\n",
              "      <th>C/V</th>\n",
              "      <th>VAGAS AC</th>\n",
              "      <th>INSCRITOS AC</th>\n",
              "      <th>C/V AC</th>\n",
              "      <th>VAGAS EP</th>\n",
              "      <th>INSCRITOS EP</th>\n",
              "      <th>C/V EP</th>\n",
              "      <th>VAGAS PPI</th>\n",
              "      <th>INSCRITOS PPI</th>\n",
              "      <th>C/V PPI</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>100</td>\n",
              "      <td>Administração, Ciências Contábeis, Economia, E...</td>\n",
              "      <td>187</td>\n",
              "      <td>1562</td>\n",
              "      <td>8,35</td>\n",
              "      <td>93</td>\n",
              "      <td>1165</td>\n",
              "      <td>12,53</td>\n",
              "      <td>59</td>\n",
              "      <td>312</td>\n",
              "      <td>5,29</td>\n",
              "      <td>35</td>\n",
              "      <td>85</td>\n",
              "      <td>2,43</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>101</td>\n",
              "      <td>Administração - Piracicaba</td>\n",
              "      <td>40</td>\n",
              "      <td>283</td>\n",
              "      <td>7,08</td>\n",
              "      <td>22</td>\n",
              "      <td>196</td>\n",
              "      <td>8,91</td>\n",
              "      <td>11</td>\n",
              "      <td>70</td>\n",
              "      <td>6,36</td>\n",
              "      <td>7</td>\n",
              "      <td>17</td>\n",
              "      <td>2,43</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>105</td>\n",
              "      <td>Arquitetura - FAU</td>\n",
              "      <td>105</td>\n",
              "      <td>2724</td>\n",
              "      <td>25,94</td>\n",
              "      <td>67</td>\n",
              "      <td>1924</td>\n",
              "      <td>28,72</td>\n",
              "      <td>27</td>\n",
              "      <td>599</td>\n",
              "      <td>22,19</td>\n",
              "      <td>11</td>\n",
              "      <td>201</td>\n",
              "      <td>18,27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>110</td>\n",
              "      <td>Arquitetura - São Carlos</td>\n",
              "      <td>31</td>\n",
              "      <td>622</td>\n",
              "      <td>20,06</td>\n",
              "      <td>15</td>\n",
              "      <td>408</td>\n",
              "      <td>27,2</td>\n",
              "      <td>10</td>\n",
              "      <td>178</td>\n",
              "      <td>17,8</td>\n",
              "      <td>6</td>\n",
              "      <td>36</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>115</td>\n",
              "      <td>Artes Cênicas</td>\n",
              "      <td>30</td>\n",
              "      <td>667</td>\n",
              "      <td>22,23</td>\n",
              "      <td>16</td>\n",
              "      <td>411</td>\n",
              "      <td>25,69</td>\n",
              "      <td>8</td>\n",
              "      <td>185</td>\n",
              "      <td>23,13</td>\n",
              "      <td>6</td>\n",
              "      <td>71</td>\n",
              "      <td>11,83</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   CODIGO  ... C/V PPI\n",
              "0     100  ...    2,43\n",
              "1     101  ...    2,43\n",
              "2     105  ...   18,27\n",
              "3     110  ...       6\n",
              "4     115  ...   11,83\n",
              "\n",
              "[5 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 97
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oRmik6RKjBqD",
        "outputId": "68630975-83bc-41c6-e67f-7c3782c70aa7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        }
      },
      "source": [
        "dados[1].head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>500</th>\n",
              "      <th>Medicina</th>\n",
              "      <th>122</th>\n",
              "      <th>15794</th>\n",
              "      <th>129,46</th>\n",
              "      <th>67</th>\n",
              "      <th>11031</th>\n",
              "      <th>164,64</th>\n",
              "      <th>33</th>\n",
              "      <th>3484</th>\n",
              "      <th>105,58</th>\n",
              "      <th>22</th>\n",
              "      <th>1279</th>\n",
              "      <th>58,14</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>501.0</td>\n",
              "      <td>Medicina - Bauru</td>\n",
              "      <td>42</td>\n",
              "      <td>5217</td>\n",
              "      <td>124,21</td>\n",
              "      <td>26</td>\n",
              "      <td>3952</td>\n",
              "      <td>152</td>\n",
              "      <td>8</td>\n",
              "      <td>887</td>\n",
              "      <td>110,88</td>\n",
              "      <td>8</td>\n",
              "      <td>378</td>\n",
              "      <td>47,25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>505.0</td>\n",
              "      <td>Medicina - Ribeirão Preto</td>\n",
              "      <td>77</td>\n",
              "      <td>6856</td>\n",
              "      <td>89,04</td>\n",
              "      <td>55</td>\n",
              "      <td>5183</td>\n",
              "      <td>94,24</td>\n",
              "      <td>18</td>\n",
              "      <td>1418</td>\n",
              "      <td>78,78</td>\n",
              "      <td>4</td>\n",
              "      <td>255</td>\n",
              "      <td>63,75</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>510.0</td>\n",
              "      <td>Medicina Veterinária</td>\n",
              "      <td>56</td>\n",
              "      <td>2403</td>\n",
              "      <td>42,91</td>\n",
              "      <td>38</td>\n",
              "      <td>1607</td>\n",
              "      <td>42,29</td>\n",
              "      <td>11</td>\n",
              "      <td>615</td>\n",
              "      <td>55,91</td>\n",
              "      <td>7</td>\n",
              "      <td>181</td>\n",
              "      <td>25,86</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>515.0</td>\n",
              "      <td>Medicina Veterinária - Pirassununga</td>\n",
              "      <td>42</td>\n",
              "      <td>906</td>\n",
              "      <td>21,57</td>\n",
              "      <td>24</td>\n",
              "      <td>624</td>\n",
              "      <td>26</td>\n",
              "      <td>10</td>\n",
              "      <td>224</td>\n",
              "      <td>22,4</td>\n",
              "      <td>8</td>\n",
              "      <td>58</td>\n",
              "      <td>7,25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>520.0</td>\n",
              "      <td>Nutrição</td>\n",
              "      <td>56</td>\n",
              "      <td>854</td>\n",
              "      <td>15,25</td>\n",
              "      <td>40</td>\n",
              "      <td>600</td>\n",
              "      <td>15</td>\n",
              "      <td>12</td>\n",
              "      <td>203</td>\n",
              "      <td>16,92</td>\n",
              "      <td>4</td>\n",
              "      <td>51</td>\n",
              "      <td>12,75</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     500                             Medicina 122  ... 22 1279  58,14\n",
              "0  501.0                     Medicina - Bauru  42  ...  8  378  47,25\n",
              "1  505.0            Medicina - Ribeirão Preto  77  ...  4  255  63,75\n",
              "2  510.0                 Medicina Veterinária  56  ...  7  181  25,86\n",
              "3  515.0  Medicina Veterinária - Pirassununga  42  ...  8   58   7,25\n",
              "4  520.0                             Nutrição  56  ...  4   51  12,75\n",
              "\n",
              "[5 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 98
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PAJdqfgFjEWa",
        "outputId": "415cc930-db85-4a12-c163-334f15ffc43e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 254
        }
      },
      "source": [
        "dados[1].tail()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>500</th>\n",
              "      <th>Medicina</th>\n",
              "      <th>122</th>\n",
              "      <th>15794</th>\n",
              "      <th>129,46</th>\n",
              "      <th>67</th>\n",
              "      <th>11031</th>\n",
              "      <th>164,64</th>\n",
              "      <th>33</th>\n",
              "      <th>3484</th>\n",
              "      <th>105,58</th>\n",
              "      <th>22</th>\n",
              "      <th>1279</th>\n",
              "      <th>58,14</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>870.0</td>\n",
              "      <td>Química (Bacharelado e Bacharelado com Atribui...</td>\n",
              "      <td>e Qua4li2d</td>\n",
              "      <td>de e Ma1te7r3i</td>\n",
              "      <td>is) -4 S,1ã2o</td>\n",
              "      <td>Carlos23</td>\n",
              "      <td>112</td>\n",
              "      <td>4,87</td>\n",
              "      <td>11</td>\n",
              "      <td>52</td>\n",
              "      <td>4,73</td>\n",
              "      <td>8</td>\n",
              "      <td>9</td>\n",
              "      <td>1,13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>875.0</td>\n",
              "      <td>Sistemas de Informação - São Carlos</td>\n",
              "      <td>35</td>\n",
              "      <td>342</td>\n",
              "      <td>9,77</td>\n",
              "      <td>19</td>\n",
              "      <td>217</td>\n",
              "      <td>11,42</td>\n",
              "      <td>10</td>\n",
              "      <td>104</td>\n",
              "      <td>10,4</td>\n",
              "      <td>6</td>\n",
              "      <td>21</td>\n",
              "      <td>3,5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>999.0</td>\n",
              "      <td>Treinamento E (Exatas)</td>\n",
              "      <td>300</td>\n",
              "      <td>3530</td>\n",
              "      <td>11,77</td>\n",
              "      <td>180</td>\n",
              "      <td>3177</td>\n",
              "      <td>17,65</td>\n",
              "      <td>75</td>\n",
              "      <td>308</td>\n",
              "      <td>4,11</td>\n",
              "      <td>45</td>\n",
              "      <td>45</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Total das Carreiras</td>\n",
              "      <td>9217</td>\n",
              "      <td>129148</td>\n",
              "      <td>14,01</td>\n",
              "      <td>5964</td>\n",
              "      <td>94888</td>\n",
              "      <td>15,91</td>\n",
              "      <td>2082</td>\n",
              "      <td>26368</td>\n",
              "      <td>12,66</td>\n",
              "      <td>1171</td>\n",
              "      <td>7892</td>\n",
              "      <td>6,74</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>51</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Total das Carreiras sem Treineiros</td>\n",
              "      <td>8317</td>\n",
              "      <td>117019</td>\n",
              "      <td>14,07</td>\n",
              "      <td>5424</td>\n",
              "      <td>83841</td>\n",
              "      <td>15,46</td>\n",
              "      <td>1857</td>\n",
              "      <td>25450</td>\n",
              "      <td>13,7</td>\n",
              "      <td>1036</td>\n",
              "      <td>7728</td>\n",
              "      <td>7,46</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      500                                           Medicina  ...  1279 58,14\n",
              "47  870.0  Química (Bacharelado e Bacharelado com Atribui...  ...     9  1,13\n",
              "48  875.0                Sistemas de Informação - São Carlos  ...    21   3,5\n",
              "49  999.0                             Treinamento E (Exatas)  ...    45     1\n",
              "50    NaN                                Total das Carreiras  ...  7892  6,74\n",
              "51    NaN                 Total das Carreiras sem Treineiros  ...  7728  7,46\n",
              "\n",
              "[5 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 99
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qy0o0Yh2jMri",
        "outputId": "7c94a31d-44df-4cd9-e7f7-c8322ce3db27",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 271
        }
      },
      "source": [
        "dados[1] = dados[1].iloc[:-2]\n",
        "dados[1].tail()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>500</th>\n",
              "      <th>Medicina</th>\n",
              "      <th>122</th>\n",
              "      <th>15794</th>\n",
              "      <th>129,46</th>\n",
              "      <th>67</th>\n",
              "      <th>11031</th>\n",
              "      <th>164,64</th>\n",
              "      <th>33</th>\n",
              "      <th>3484</th>\n",
              "      <th>105,58</th>\n",
              "      <th>22</th>\n",
              "      <th>1279</th>\n",
              "      <th>58,14</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>855.0</td>\n",
              "      <td>Química - Bacharelado e Licenciatura</td>\n",
              "      <td>84</td>\n",
              "      <td>752</td>\n",
              "      <td>8,95</td>\n",
              "      <td>50</td>\n",
              "      <td>462</td>\n",
              "      <td>9,24</td>\n",
              "      <td>20</td>\n",
              "      <td>220</td>\n",
              "      <td>11</td>\n",
              "      <td>14</td>\n",
              "      <td>70</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>865.0</td>\n",
              "      <td>Química - Licenciatura - Ribeirão Preto</td>\n",
              "      <td>28</td>\n",
              "      <td>61</td>\n",
              "      <td>2,18</td>\n",
              "      <td>15</td>\n",
              "      <td>46</td>\n",
              "      <td>3,07</td>\n",
              "      <td>8</td>\n",
              "      <td>10</td>\n",
              "      <td>1,25</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>870.0</td>\n",
              "      <td>Química (Bacharelado e Bacharelado com Atribui...</td>\n",
              "      <td>e Qua4li2d</td>\n",
              "      <td>de e Ma1te7r3i</td>\n",
              "      <td>is) -4 S,1ã2o</td>\n",
              "      <td>Carlos23</td>\n",
              "      <td>112</td>\n",
              "      <td>4,87</td>\n",
              "      <td>11</td>\n",
              "      <td>52</td>\n",
              "      <td>4,73</td>\n",
              "      <td>8</td>\n",
              "      <td>9</td>\n",
              "      <td>1,13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>875.0</td>\n",
              "      <td>Sistemas de Informação - São Carlos</td>\n",
              "      <td>35</td>\n",
              "      <td>342</td>\n",
              "      <td>9,77</td>\n",
              "      <td>19</td>\n",
              "      <td>217</td>\n",
              "      <td>11,42</td>\n",
              "      <td>10</td>\n",
              "      <td>104</td>\n",
              "      <td>10,4</td>\n",
              "      <td>6</td>\n",
              "      <td>21</td>\n",
              "      <td>3,5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>999.0</td>\n",
              "      <td>Treinamento E (Exatas)</td>\n",
              "      <td>300</td>\n",
              "      <td>3530</td>\n",
              "      <td>11,77</td>\n",
              "      <td>180</td>\n",
              "      <td>3177</td>\n",
              "      <td>17,65</td>\n",
              "      <td>75</td>\n",
              "      <td>308</td>\n",
              "      <td>4,11</td>\n",
              "      <td>45</td>\n",
              "      <td>45</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      500                                           Medicina  ... 1279 58,14\n",
              "45  855.0               Química - Bacharelado e Licenciatura  ...   70     5\n",
              "46  865.0            Química - Licenciatura - Ribeirão Preto  ...    5     1\n",
              "47  870.0  Química (Bacharelado e Bacharelado com Atribui...  ...    9  1,13\n",
              "48  875.0                Sistemas de Informação - São Carlos  ...   21   3,5\n",
              "49  999.0                             Treinamento E (Exatas)  ...   45     1\n",
              "\n",
              "[5 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 100
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-f20ExGGjSKz",
        "outputId": "fa9e4e80-b79e-4b22-90cb-bce09bbb2038",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 288
        }
      },
      "source": [
        "dados_columns = dados[0].columns\n",
        "linha_extra = dados[1].columns\n",
        "dados[1].columns = dados_columns\n",
        "dados[1].head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>CODIGO</th>\n",
              "      <th>NOME</th>\n",
              "      <th>VAGAS</th>\n",
              "      <th>INSCRITOS</th>\n",
              "      <th>C/V</th>\n",
              "      <th>VAGAS AC</th>\n",
              "      <th>INSCRITOS AC</th>\n",
              "      <th>C/V AC</th>\n",
              "      <th>VAGAS EP</th>\n",
              "      <th>INSCRITOS EP</th>\n",
              "      <th>C/V EP</th>\n",
              "      <th>VAGAS PPI</th>\n",
              "      <th>INSCRITOS PPI</th>\n",
              "      <th>C/V PPI</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>501.0</td>\n",
              "      <td>Medicina - Bauru</td>\n",
              "      <td>42</td>\n",
              "      <td>5217</td>\n",
              "      <td>124,21</td>\n",
              "      <td>26</td>\n",
              "      <td>3952</td>\n",
              "      <td>152</td>\n",
              "      <td>8</td>\n",
              "      <td>887</td>\n",
              "      <td>110,88</td>\n",
              "      <td>8</td>\n",
              "      <td>378</td>\n",
              "      <td>47,25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>505.0</td>\n",
              "      <td>Medicina - Ribeirão Preto</td>\n",
              "      <td>77</td>\n",
              "      <td>6856</td>\n",
              "      <td>89,04</td>\n",
              "      <td>55</td>\n",
              "      <td>5183</td>\n",
              "      <td>94,24</td>\n",
              "      <td>18</td>\n",
              "      <td>1418</td>\n",
              "      <td>78,78</td>\n",
              "      <td>4</td>\n",
              "      <td>255</td>\n",
              "      <td>63,75</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>510.0</td>\n",
              "      <td>Medicina Veterinária</td>\n",
              "      <td>56</td>\n",
              "      <td>2403</td>\n",
              "      <td>42,91</td>\n",
              "      <td>38</td>\n",
              "      <td>1607</td>\n",
              "      <td>42,29</td>\n",
              "      <td>11</td>\n",
              "      <td>615</td>\n",
              "      <td>55,91</td>\n",
              "      <td>7</td>\n",
              "      <td>181</td>\n",
              "      <td>25,86</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>515.0</td>\n",
              "      <td>Medicina Veterinária - Pirassununga</td>\n",
              "      <td>42</td>\n",
              "      <td>906</td>\n",
              "      <td>21,57</td>\n",
              "      <td>24</td>\n",
              "      <td>624</td>\n",
              "      <td>26</td>\n",
              "      <td>10</td>\n",
              "      <td>224</td>\n",
              "      <td>22,4</td>\n",
              "      <td>8</td>\n",
              "      <td>58</td>\n",
              "      <td>7,25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>520.0</td>\n",
              "      <td>Nutrição</td>\n",
              "      <td>56</td>\n",
              "      <td>854</td>\n",
              "      <td>15,25</td>\n",
              "      <td>40</td>\n",
              "      <td>600</td>\n",
              "      <td>15</td>\n",
              "      <td>12</td>\n",
              "      <td>203</td>\n",
              "      <td>16,92</td>\n",
              "      <td>4</td>\n",
              "      <td>51</td>\n",
              "      <td>12,75</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   CODIGO                                 NOME  ... INSCRITOS PPI C/V PPI\n",
              "0   501.0                     Medicina - Bauru  ...           378   47,25\n",
              "1   505.0            Medicina - Ribeirão Preto  ...           255   63,75\n",
              "2   510.0                 Medicina Veterinária  ...           181   25,86\n",
              "3   515.0  Medicina Veterinária - Pirassununga  ...            58    7,25\n",
              "4   520.0                             Nutrição  ...            51   12,75\n",
              "\n",
              "[5 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 101
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2wOJVV0vjel7",
        "outputId": "f4a0a7c8-815b-41d9-a1bc-311cbef1b27c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 96
        }
      },
      "source": [
        "dados.append(pd.DataFrame([linha_extra], columns=dados_columns))\n",
        "dados[2].head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>CODIGO</th>\n",
              "      <th>NOME</th>\n",
              "      <th>VAGAS</th>\n",
              "      <th>INSCRITOS</th>\n",
              "      <th>C/V</th>\n",
              "      <th>VAGAS AC</th>\n",
              "      <th>INSCRITOS AC</th>\n",
              "      <th>C/V AC</th>\n",
              "      <th>VAGAS EP</th>\n",
              "      <th>INSCRITOS EP</th>\n",
              "      <th>C/V EP</th>\n",
              "      <th>VAGAS PPI</th>\n",
              "      <th>INSCRITOS PPI</th>\n",
              "      <th>C/V PPI</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>500</td>\n",
              "      <td>Medicina</td>\n",
              "      <td>122</td>\n",
              "      <td>15794</td>\n",
              "      <td>129,46</td>\n",
              "      <td>67</td>\n",
              "      <td>11031</td>\n",
              "      <td>164,64</td>\n",
              "      <td>33</td>\n",
              "      <td>3484</td>\n",
              "      <td>105,58</td>\n",
              "      <td>22</td>\n",
              "      <td>1279</td>\n",
              "      <td>58,14</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  CODIGO      NOME VAGAS INSCRITOS  ...  C/V EP VAGAS PPI INSCRITOS PPI C/V PPI\n",
              "0    500  Medicina   122     15794  ...  105,58        22          1279   58,14\n",
              "\n",
              "[1 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 102
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V8VOz3omjs3c",
        "outputId": "4ca8de4e-7467-4b5d-d804-fb488ffa8a22",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 288
        }
      },
      "source": [
        "dados = pd.concat(dados)\n",
        "dados.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>CODIGO</th>\n",
              "      <th>NOME</th>\n",
              "      <th>VAGAS</th>\n",
              "      <th>INSCRITOS</th>\n",
              "      <th>C/V</th>\n",
              "      <th>VAGAS AC</th>\n",
              "      <th>INSCRITOS AC</th>\n",
              "      <th>C/V AC</th>\n",
              "      <th>VAGAS EP</th>\n",
              "      <th>INSCRITOS EP</th>\n",
              "      <th>C/V EP</th>\n",
              "      <th>VAGAS PPI</th>\n",
              "      <th>INSCRITOS PPI</th>\n",
              "      <th>C/V PPI</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>100</td>\n",
              "      <td>Administração, Ciências Contábeis, Economia, E...</td>\n",
              "      <td>187</td>\n",
              "      <td>1562</td>\n",
              "      <td>8,35</td>\n",
              "      <td>93</td>\n",
              "      <td>1165</td>\n",
              "      <td>12,53</td>\n",
              "      <td>59</td>\n",
              "      <td>312</td>\n",
              "      <td>5,29</td>\n",
              "      <td>35</td>\n",
              "      <td>85</td>\n",
              "      <td>2,43</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>101</td>\n",
              "      <td>Administração - Piracicaba</td>\n",
              "      <td>40</td>\n",
              "      <td>283</td>\n",
              "      <td>7,08</td>\n",
              "      <td>22</td>\n",
              "      <td>196</td>\n",
              "      <td>8,91</td>\n",
              "      <td>11</td>\n",
              "      <td>70</td>\n",
              "      <td>6,36</td>\n",
              "      <td>7</td>\n",
              "      <td>17</td>\n",
              "      <td>2,43</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>105</td>\n",
              "      <td>Arquitetura - FAU</td>\n",
              "      <td>105</td>\n",
              "      <td>2724</td>\n",
              "      <td>25,94</td>\n",
              "      <td>67</td>\n",
              "      <td>1924</td>\n",
              "      <td>28,72</td>\n",
              "      <td>27</td>\n",
              "      <td>599</td>\n",
              "      <td>22,19</td>\n",
              "      <td>11</td>\n",
              "      <td>201</td>\n",
              "      <td>18,27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>110</td>\n",
              "      <td>Arquitetura - São Carlos</td>\n",
              "      <td>31</td>\n",
              "      <td>622</td>\n",
              "      <td>20,06</td>\n",
              "      <td>15</td>\n",
              "      <td>408</td>\n",
              "      <td>27,2</td>\n",
              "      <td>10</td>\n",
              "      <td>178</td>\n",
              "      <td>17,8</td>\n",
              "      <td>6</td>\n",
              "      <td>36</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>115</td>\n",
              "      <td>Artes Cênicas</td>\n",
              "      <td>30</td>\n",
              "      <td>667</td>\n",
              "      <td>22,23</td>\n",
              "      <td>16</td>\n",
              "      <td>411</td>\n",
              "      <td>25,69</td>\n",
              "      <td>8</td>\n",
              "      <td>185</td>\n",
              "      <td>23,13</td>\n",
              "      <td>6</td>\n",
              "      <td>71</td>\n",
              "      <td>11,83</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  CODIGO  ... C/V PPI\n",
              "0    100  ...    2,43\n",
              "1    101  ...    2,43\n",
              "2    105  ...   18,27\n",
              "3    110  ...       6\n",
              "4    115  ...   11,83\n",
              "\n",
              "[5 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 103
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RR_74zNjj0TL",
        "outputId": "a24e7c7f-3152-48f9-a3b7-90530739f523",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "len(dados)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "109"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 104
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m-TzsSloj1mD",
        "outputId": "6d6065ca-45ed-4190-94d4-ecaf2e76a672",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        }
      },
      "source": [
        "dados = dados[[\"NOME\", \"VAGAS\", \"INSCRITOS\", \"C/V\"]]\n",
        "dados.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>NOME</th>\n",
              "      <th>VAGAS</th>\n",
              "      <th>INSCRITOS</th>\n",
              "      <th>C/V</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Administração, Ciências Contábeis, Economia, E...</td>\n",
              "      <td>187</td>\n",
              "      <td>1562</td>\n",
              "      <td>8,35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Administração - Piracicaba</td>\n",
              "      <td>40</td>\n",
              "      <td>283</td>\n",
              "      <td>7,08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Arquitetura - FAU</td>\n",
              "      <td>105</td>\n",
              "      <td>2724</td>\n",
              "      <td>25,94</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Arquitetura - São Carlos</td>\n",
              "      <td>31</td>\n",
              "      <td>622</td>\n",
              "      <td>20,06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Artes Cênicas</td>\n",
              "      <td>30</td>\n",
              "      <td>667</td>\n",
              "      <td>22,23</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                NOME VAGAS INSCRITOS    C/V\n",
              "0  Administração, Ciências Contábeis, Economia, E...   187      1562   8,35\n",
              "1                         Administração - Piracicaba    40       283   7,08\n",
              "2                                  Arquitetura - FAU   105      2724  25,94\n",
              "3                           Arquitetura - São Carlos    31       622  20,06\n",
              "4                                      Artes Cênicas    30       667  22,23"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 105
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "18N8vkkBkF9D",
        "outputId": "675a5d9e-1215-4ef1-c900-e08c2698bbd4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 172
        }
      },
      "source": [
        "dados.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>NOME</th>\n",
              "      <th>VAGAS</th>\n",
              "      <th>INSCRITOS</th>\n",
              "      <th>C/V</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>109</td>\n",
              "      <td>109</td>\n",
              "      <td>109</td>\n",
              "      <td>109</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>unique</th>\n",
              "      <td>109</td>\n",
              "      <td>60</td>\n",
              "      <td>106</td>\n",
              "      <td>104</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>top</th>\n",
              "      <td>Nutrição e Metabolismo - Ribeirão Preto</td>\n",
              "      <td>28</td>\n",
              "      <td>158</td>\n",
              "      <td>3,71</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>freq</th>\n",
              "      <td>1</td>\n",
              "      <td>9</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                           NOME  VAGAS INSCRITOS   C/V\n",
              "count                                       109    109       109   109\n",
              "unique                                      109     60       106   104\n",
              "top     Nutrição e Metabolismo - Ribeirão Preto     28       158  3,71\n",
              "freq                                          1      9         2     2"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 106
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RwgV4WzskHJz",
        "outputId": "276a3e1c-0215-4a75-db20-fd28ca8112cf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        }
      },
      "source": [
        "dados.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 109 entries, 0 to 0\n",
            "Data columns (total 4 columns):\n",
            " #   Column     Non-Null Count  Dtype \n",
            "---  ------     --------------  ----- \n",
            " 0   NOME       109 non-null    object\n",
            " 1   VAGAS      109 non-null    object\n",
            " 2   INSCRITOS  109 non-null    object\n",
            " 3   C/V        109 non-null    object\n",
            "dtypes: object(4)\n",
            "memory usage: 4.3+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JV2fN__BkMfj",
        "outputId": "711dbbdf-fa80-4eb5-fdc6-950ca62d0bbe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        }
      },
      "source": [
        "dados.tail()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>NOME</th>\n",
              "      <th>VAGAS</th>\n",
              "      <th>INSCRITOS</th>\n",
              "      <th>C/V</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>Química - Licenciatura - Ribeirão Preto</td>\n",
              "      <td>28</td>\n",
              "      <td>61</td>\n",
              "      <td>2,18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>Química (Bacharelado e Bacharelado com Atribui...</td>\n",
              "      <td>e Qua4li2d</td>\n",
              "      <td>de e Ma1te7r3i</td>\n",
              "      <td>is) -4 S,1ã2o</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>Sistemas de Informação - São Carlos</td>\n",
              "      <td>35</td>\n",
              "      <td>342</td>\n",
              "      <td>9,77</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>Treinamento E (Exatas)</td>\n",
              "      <td>300</td>\n",
              "      <td>3530</td>\n",
              "      <td>11,77</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Medicina</td>\n",
              "      <td>122</td>\n",
              "      <td>15794</td>\n",
              "      <td>129,46</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                 NOME  ...            C/V\n",
              "46            Química - Licenciatura - Ribeirão Preto  ...           2,18\n",
              "47  Química (Bacharelado e Bacharelado com Atribui...  ...  is) -4 S,1ã2o\n",
              "48                Sistemas de Informação - São Carlos  ...           9,77\n",
              "49                             Treinamento E (Exatas)  ...          11,77\n",
              "0                                            Medicina  ...         129,46\n",
              "\n",
              "[5 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 108
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dwNdXQcAkXkL",
        "outputId": "a7f1ffc9-d6d7-4c4e-8a80-02c7efa59714",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        }
      },
      "source": [
        "dados = dados.drop(47)\n",
        "dados.tail()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>NOME</th>\n",
              "      <th>VAGAS</th>\n",
              "      <th>INSCRITOS</th>\n",
              "      <th>C/V</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>Química - Bacharelado e Licenciatura</td>\n",
              "      <td>84</td>\n",
              "      <td>752</td>\n",
              "      <td>8,95</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>Química - Licenciatura - Ribeirão Preto</td>\n",
              "      <td>28</td>\n",
              "      <td>61</td>\n",
              "      <td>2,18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>Sistemas de Informação - São Carlos</td>\n",
              "      <td>35</td>\n",
              "      <td>342</td>\n",
              "      <td>9,77</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>Treinamento E (Exatas)</td>\n",
              "      <td>300</td>\n",
              "      <td>3530</td>\n",
              "      <td>11,77</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Medicina</td>\n",
              "      <td>122</td>\n",
              "      <td>15794</td>\n",
              "      <td>129,46</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                       NOME VAGAS INSCRITOS     C/V\n",
              "45     Química - Bacharelado e Licenciatura    84       752    8,95\n",
              "46  Química - Licenciatura - Ribeirão Preto    28        61    2,18\n",
              "48      Sistemas de Informação - São Carlos    35       342    9,77\n",
              "49                   Treinamento E (Exatas)   300      3530   11,77\n",
              "0                                  Medicina   122     15794  129,46"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 109
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6riiOoz2kZp0",
        "outputId": "fbdb0f5a-20aa-4093-d0e3-df06fcd1d85e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 172
        }
      },
      "source": [
        "dados.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>NOME</th>\n",
              "      <th>VAGAS</th>\n",
              "      <th>INSCRITOS</th>\n",
              "      <th>C/V</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>107</td>\n",
              "      <td>107</td>\n",
              "      <td>107</td>\n",
              "      <td>107</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>unique</th>\n",
              "      <td>107</td>\n",
              "      <td>58</td>\n",
              "      <td>104</td>\n",
              "      <td>102</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>top</th>\n",
              "      <td>Nutrição e Metabolismo - Ribeirão Preto</td>\n",
              "      <td>28</td>\n",
              "      <td>158</td>\n",
              "      <td>6,04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>freq</th>\n",
              "      <td>1</td>\n",
              "      <td>9</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                           NOME  VAGAS INSCRITOS   C/V\n",
              "count                                       107    107       107   107\n",
              "unique                                      107     58       104   102\n",
              "top     Nutrição e Metabolismo - Ribeirão Preto     28       158  6,04\n",
              "freq                                          1      9         2     2"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 110
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CZhrRO8gkdXU",
        "outputId": "4b3b3875-8c4c-4d66-e14b-f5e169d929dd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "dados[\"candidatos\"] = pd.to_numeric(dados[\"INSCRITOS\"], errors=\"coerce\")\n",
        "dados[\"vagas\"] = pd.to_numeric(dados[\"VAGAS\"], errors=\"coerce\")\n",
        "dados[\"candidatos_por_vaga\"] = dados[\"candidatos\"] / dados[\"vagas\"]\n",
        "dados[\"nome\"] = dados[\"NOME\"]\n",
        "dados = dados.dropna()\n",
        "dados[\"candidatos\"] = dados[\"candidatos\"].astype(int)\n",
        "dados[\"vagas\"] = dados[\"vagas\"].astype(int)\n",
        "dados.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>candidatos</th>\n",
              "      <th>vagas</th>\n",
              "      <th>candidatos_por_vaga</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>105.000000</td>\n",
              "      <td>105.000000</td>\n",
              "      <td>105.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1193.066667</td>\n",
              "      <td>81.771429</td>\n",
              "      <td>15.273537</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>2181.669426</td>\n",
              "      <td>115.123534</td>\n",
              "      <td>21.092466</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>45.000000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>1.607143</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>163.000000</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>5.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>483.000000</td>\n",
              "      <td>42.000000</td>\n",
              "      <td>8.352941</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1092.000000</td>\n",
              "      <td>84.000000</td>\n",
              "      <td>15.116667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>15794.000000</td>\n",
              "      <td>783.000000</td>\n",
              "      <td>129.459016</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         candidatos       vagas  candidatos_por_vaga\n",
              "count    105.000000  105.000000           105.000000\n",
              "mean    1193.066667   81.771429            15.273537\n",
              "std     2181.669426  115.123534            21.092466\n",
              "min       45.000000   12.000000             1.607143\n",
              "25%      163.000000   28.000000             5.000000\n",
              "50%      483.000000   42.000000             8.352941\n",
              "75%     1092.000000   84.000000            15.116667\n",
              "max    15794.000000  783.000000           129.459016"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 112
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1oliv9BykkvL",
        "outputId": "0d226d03-ac1b-431a-a062-188e80787d22",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 274
        }
      },
      "source": [
        "dados.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 105 entries, 0 to 0\n",
            "Data columns (total 8 columns):\n",
            " #   Column               Non-Null Count  Dtype  \n",
            "---  ------               --------------  -----  \n",
            " 0   NOME                 105 non-null    object \n",
            " 1   VAGAS                105 non-null    object \n",
            " 2   INSCRITOS            105 non-null    object \n",
            " 3   C/V                  105 non-null    object \n",
            " 4   candidatos           105 non-null    int64  \n",
            " 5   vagas                105 non-null    int64  \n",
            " 6   candidatos_por_vaga  105 non-null    float64\n",
            " 7   nome                 105 non-null    object \n",
            "dtypes: float64(1), int64(2), object(5)\n",
            "memory usage: 7.4+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4CAs2kZYlEUk",
        "outputId": "f6cffe83-66db-46ea-84c7-abae832c7ae0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 974
        }
      },
      "source": [
        "dados = dados[[\"nome\", \"vagas\", \"candidatos\", \"candidatos_por_vaga\"]]\n",
        "dados = dados.sort_values(\"candidatos_por_vaga\", ascending=False)\n",
        "dados.head(30)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>nome</th>\n",
              "      <th>vagas</th>\n",
              "      <th>candidatos</th>\n",
              "      <th>candidatos_por_vaga</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Medicina</td>\n",
              "      <td>122</td>\n",
              "      <td>15794</td>\n",
              "      <td>129.459016</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Medicina - Bauru</td>\n",
              "      <td>42</td>\n",
              "      <td>5217</td>\n",
              "      <td>124.214286</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Medicina - Ribeirão Preto</td>\n",
              "      <td>77</td>\n",
              "      <td>6856</td>\n",
              "      <td>89.038961</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Psicologia</td>\n",
              "      <td>49</td>\n",
              "      <td>3610</td>\n",
              "      <td>73.673469</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>Relações Internacionais</td>\n",
              "      <td>42</td>\n",
              "      <td>2461</td>\n",
              "      <td>58.595238</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Curso Superior do Audiovisual</td>\n",
              "      <td>24</td>\n",
              "      <td>1126</td>\n",
              "      <td>46.916667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Psicologia - Ribeirão Preto</td>\n",
              "      <td>29</td>\n",
              "      <td>1254</td>\n",
              "      <td>43.241379</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Medicina Veterinária</td>\n",
              "      <td>56</td>\n",
              "      <td>2403</td>\n",
              "      <td>42.910714</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>Ciências Biomédicas</td>\n",
              "      <td>28</td>\n",
              "      <td>1042</td>\n",
              "      <td>37.214286</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Design</td>\n",
              "      <td>28</td>\n",
              "      <td>906</td>\n",
              "      <td>32.357143</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>Publicidade e Propaganda</td>\n",
              "      <td>35</td>\n",
              "      <td>1092</td>\n",
              "      <td>31.200000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>52</th>\n",
              "      <td>Fisioterapia</td>\n",
              "      <td>18</td>\n",
              "      <td>561</td>\n",
              "      <td>31.166667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>Jornalismo</td>\n",
              "      <td>48</td>\n",
              "      <td>1397</td>\n",
              "      <td>29.104167</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Artes Visuais</td>\n",
              "      <td>30</td>\n",
              "      <td>797</td>\n",
              "      <td>26.566667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Arquitetura - FAU</td>\n",
              "      <td>105</td>\n",
              "      <td>2724</td>\n",
              "      <td>25.942857</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>Engenharia Aeronáutica - São Carlos</td>\n",
              "      <td>33</td>\n",
              "      <td>823</td>\n",
              "      <td>24.939394</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Direito</td>\n",
              "      <td>393</td>\n",
              "      <td>9461</td>\n",
              "      <td>24.073791</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Artes Cênicas</td>\n",
              "      <td>30</td>\n",
              "      <td>667</td>\n",
              "      <td>22.233333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Medicina Veterinária - Pirassununga</td>\n",
              "      <td>42</td>\n",
              "      <td>906</td>\n",
              "      <td>21.571429</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Arquitetura - São Carlos</td>\n",
              "      <td>31</td>\n",
              "      <td>622</td>\n",
              "      <td>20.064516</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>Ciências Biológicas</td>\n",
              "      <td>84</td>\n",
              "      <td>1467</td>\n",
              "      <td>17.464286</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>Ciências Biomédicas - Ribeirão Preto</td>\n",
              "      <td>20</td>\n",
              "      <td>347</td>\n",
              "      <td>17.350000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>53</th>\n",
              "      <td>Fisioterapia - Ribeirão Preto</td>\n",
              "      <td>30</td>\n",
              "      <td>505</td>\n",
              "      <td>16.833333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Odontologia</td>\n",
              "      <td>93</td>\n",
              "      <td>1437</td>\n",
              "      <td>15.451613</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>Engenharia Civil - São Carlos</td>\n",
              "      <td>55</td>\n",
              "      <td>849</td>\n",
              "      <td>15.436364</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Nutrição</td>\n",
              "      <td>56</td>\n",
              "      <td>854</td>\n",
              "      <td>15.250000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>Treinamento B (Biológicas)</td>\n",
              "      <td>300</td>\n",
              "      <td>4535</td>\n",
              "      <td>15.116667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Odontologia - Ribeirão Preto</td>\n",
              "      <td>56</td>\n",
              "      <td>820</td>\n",
              "      <td>14.642857</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Economia, Administração, Ciências Contábeis e ...</td>\n",
              "      <td>413</td>\n",
              "      <td>5794</td>\n",
              "      <td>14.029056</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>Relações Públicas</td>\n",
              "      <td>35</td>\n",
              "      <td>486</td>\n",
              "      <td>13.885714</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                 nome  ...  candidatos_por_vaga\n",
              "0                                            Medicina  ...           129.459016\n",
              "0                                    Medicina - Bauru  ...           124.214286\n",
              "1                           Medicina - Ribeirão Preto  ...            89.038961\n",
              "10                                         Psicologia  ...            73.673469\n",
              "31                            Relações Internacionais  ...            58.595238\n",
              "9                       Curso Superior do Audiovisual  ...            46.916667\n",
              "11                        Psicologia - Ribeirão Preto  ...            43.241379\n",
              "2                                Medicina Veterinária  ...            42.910714\n",
              "40                                Ciências Biomédicas  ...            37.214286\n",
              "10                                             Design  ...            32.357143\n",
              "30                           Publicidade e Propaganda  ...            31.200000\n",
              "52                                       Fisioterapia  ...            31.166667\n",
              "21                                         Jornalismo  ...            29.104167\n",
              "5                                       Artes Visuais  ...            26.566667\n",
              "2                                   Arquitetura - FAU  ...            25.942857\n",
              "20                Engenharia Aeronáutica - São Carlos  ...            24.939394\n",
              "11                                            Direito  ...            24.073791\n",
              "4                                       Artes Cênicas  ...            22.233333\n",
              "3                 Medicina Veterinária - Pirassununga  ...            21.571429\n",
              "3                            Arquitetura - São Carlos  ...            20.064516\n",
              "37                                Ciências Biológicas  ...            17.464286\n",
              "41               Ciências Biomédicas - Ribeirão Preto  ...            17.350000\n",
              "53                      Fisioterapia - Ribeirão Preto  ...            16.833333\n",
              "7                                         Odontologia  ...            15.451613\n",
              "24                      Engenharia Civil - São Carlos  ...            15.436364\n",
              "4                                            Nutrição  ...            15.250000\n",
              "16                         Treinamento B (Biológicas)  ...            15.116667\n",
              "9                        Odontologia - Ribeirão Preto  ...            14.642857\n",
              "12  Economia, Administração, Ciências Contábeis e ...  ...            14.029056\n",
              "32                                  Relações Públicas  ...            13.885714\n",
              "\n",
              "[30 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 114
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w5FZ9Hw6lXaE",
        "outputId": "eb3328cd-e086-4eeb-e048-4599d304e01d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "curso = 'Medicina'\n",
        "linha = dados.query(\"nome==@curso\").iloc[0]\n",
        "vagas = linha['vagas']\n",
        "candidatos = linha['candidatos']\n",
        "vagas, candidatos"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(122, 15794)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 115
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "edtlirOllpis",
        "outputId": "854ffb33-e7ab-4512-cb93-c132f4938a69",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "import numpy as np\n",
        "\n",
        "np.random.seed(537846)\n",
        "\n",
        "def gera(media, desvio_padrao, candidatos):\n",
        "  return np.random.normal(media, desvio_padrao, candidatos)\n",
        "\n",
        "proprio_esforco = gera(70, 10, candidatos)\n",
        "proprio_esforco"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([68.54575938, 73.8392757 , 52.17456233, ..., 80.92326482,\n",
              "       64.31312253, 72.88962942])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 116
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "raIeWph5mED-",
        "outputId": "f16413c1-0b67-46f7-8f8a-b94f1e931716",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        }
      },
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.figure(figsize=(12,6))\n",
        "sns.distplot(proprio_esforco)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AY9rEQ7LmhjD",
        "outputId": "29540aae-e498-4a7c-bcef-cb1942bdc7e8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "import numpy as np\n",
        "\n",
        "def gera_sorte(candidatos):\n",
        "  # DESAFIO: inserir a desigualdade\n",
        "  return np.random.uniform(0, 100, candidatos)\n",
        "\n",
        "sorte = gera_sorte(candidatos)\n",
        "sorte"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([42.03700341, 67.04055542, 50.72347161, ..., 50.16007514,\n",
              "       50.86301304, 68.26186712])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 118
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A-NL0ZHYnA7N",
        "outputId": "d4e43490-dc5e-4571-b1b1-de7e2c2d43e6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        }
      },
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.figure(figsize=(12,6))\n",
        "sns.distplot(sorte)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6kn6CsL8nCyk"
      },
      "source": [
        "def gera_df_candidatos(proprio_esforco, sorte, vagas, fator_sorte = 0.05, identificador = None):\n",
        "  dados_gerais = np.array([proprio_esforco, sorte]).T\n",
        "  candidatos_gerais = pd.DataFrame(dados_gerais, columns=[\"proprio_esforco\", \"sorte\"])\n",
        "  candidatos_gerais = candidatos_gerais.sort_values(\"proprio_esforco\", ascending=False)\n",
        "  if identificador is None:\n",
        "    identificador = range(len(candidatos_gerais))\n",
        "  candidatos_gerais[\"identificador\"] = identificador\n",
        "  candidatos_gerais[\"nota_final\"] = candidatos_gerais[\"proprio_esforco\"] * (1-fator_sorte) + candidatos_gerais[\"sorte\"] * fator_sorte\n",
        "\n",
        "  selecionadas_esforco_e_sorte = candidatos_gerais.sort_values(\"nota_final\", ascending=False).head(vagas)[\"identificador\"]\n",
        "  selecionadas_esforco = candidatos_gerais.head(vagas)[\"identificador\"]\n",
        "  return candidatos_gerais, selecionadas_esforco, selecionadas_esforco_e_sorte"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0nSPgIqOnjId",
        "outputId": "dab3b9c5-1bf6-43bd-fec5-6c93de0bed95",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 411
        }
      },
      "source": [
        "candidatos_gerais, selecionadas_esforco, selecionadas_esforco_sorte = gera_df_candidatos(proprio_esforco, sorte, vagas, 0.05)\n",
        "selecionadas_esforco, selecionadas_esforco_sorte"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1132       0\n",
              " 4149       1\n",
              " 14303      2\n",
              " 6593       3\n",
              " 11130      4\n",
              "         ... \n",
              " 10862    117\n",
              " 14045    118\n",
              " 13295    119\n",
              " 12156    120\n",
              " 10617    121\n",
              " Name: identificador, Length: 122, dtype: int64, 1132       0\n",
              " 4149       1\n",
              " 14303      2\n",
              " 6593       3\n",
              " 11130      4\n",
              "         ... \n",
              " 5151      93\n",
              " 15662     85\n",
              " 13536    203\n",
              " 15311    142\n",
              " 7858     106\n",
              " Name: identificador, Length: 122, dtype: int64)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 142
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UbDMrp1JpNH0",
        "outputId": "834da545-1519-4732-dd2b-41269671a42d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        }
      },
      "source": [
        "def calcula_resultado(candidatos, vagas, conjunto1, conjunto2, debug = True):\n",
        "  comum = len(np.intersect1d(conjunto1, conjunto2))\n",
        "  diferentes = vagas - comum\n",
        "  proporcao_esforco_e_sorte = diferentes / vagas * 100\n",
        "  if debug:\n",
        "    print(f\"Com {candidatos} candidatas, {vagas} vagas, somente {comum} passaram por 'puro esforco', outras {diferentes} passaram com esforco e sorte. {proporcao_esforco_e_sorte:.4}% precisaram de sorte.\")\n",
        "  return proporcao_esforco_e_sorte\n",
        "\n",
        "calcula_resultado(candidatos, vagas, selecionadas_esforco, selecionadas_esforco_sorte)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Com 15794 candidatas, 122 vagas, somente 101 passaram por 'puro esforco', outras 21 passaram com esforco e sorte. 17.21% precisaram de sorte.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "17.21311475409836"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 156
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OJGSYHEeqSuE"
      },
      "source": [
        "# DESAFIO: parsear\n",
        "# https://www.fuvest.br/wp-content/uploads/fuvest_2020_nota_de_corte.pdf"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MpJz9e1JapUC"
      },
      "source": [
        "corte_segunda_fase = 303+141+88\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ADjrcSsuaW9N",
        "outputId": "7bef71d2-d0ff-45bd-96a5-eddd95b3d0cb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "\n",
        "def simulacao_inteira(candidatos: int, corte_segunda_fase: int, vagas: int, fator_sorte: float):\n",
        "  proprio_esforco = gera(70, 10, candidatos)\n",
        "  sorte = gera_sorte(candidatos)\n",
        "  candidatos_gerais_fase1, selecionadas_esforco_fase1, selecionadas_esforco_sorte_fase1 = gera_df_candidatos(proprio_esforco, sorte, corte_segunda_fase, fator_sorte)\n",
        "\n",
        "  resultado_fase_1_geral = candidatos_gerais_fase1.set_index(\"identificador\")\n",
        "  \n",
        "  selecionadas_esforco_final = selecionadas_esforco_fase1[:vagas]\n",
        "\n",
        "  candidatos_fase_2 = resultado_fase_1_geral.loc[selecionadas_esforco_sorte_fase1]\n",
        "  _, _, selecionadas_esforco_sorte_fase2 = gera_df_candidatos(candidatos_fase_2[\"proprio_esforco\"], candidatos_fase_2[\"sorte\"], vagas, fator_sorte, candidatos_fase_2.index)\n",
        "  return calcula_resultado(candidatos, vagas, selecionadas_esforco_final, selecionadas_esforco_sorte_fase2, debug=False)\n",
        "\n",
        "\n",
        "simulacao_inteira(candidatos, corte_segunda_fase, vagas, 0.05)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "29.508196721311474"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 160
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vCHMqn3usR8v"
      },
      "source": [
        "from tqdm import tqdm"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HJ8roMCzd0fk",
        "outputId": "a239422c-049e-4472-903e-31bb2d095bef",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 387
        }
      },
      "source": [
        "porcentagem_precisou_de_sorte = [simulacao_inteira(candidatos, corte_segunda_fase, vagas, 0.05) for i in tqdm(range(1000))]\n",
        "print(np.mean(porcentagem_precisou_de_sorte))\n",
        "plt.title(\"Medicina\")\n",
        "sns.distplot(porcentagem_precisou_de_sorte)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 1000/1000 [00:14<00:00, 68.26it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "23.458196721311477\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "/usr/local/lib/python3.6/dist-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fb92587db38>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 166
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mHlw19mheg7N",
        "outputId": "e0cb46f4-bc64-4457-fb51-72d25ea2b2b4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 387
        }
      },
      "source": [
        "porcentagem_precisou_de_sorte = [simulacao_inteira(2125+943+233, 648+287+149, 264, 0.05) for i in tqdm(range(1000))]\n",
        "print(np.mean(porcentagem_precisou_de_sorte))\n",
        "plt.title(\"Computação\")\n",
        "sns.distplot(porcentagem_precisou_de_sorte)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 1000/1000 [00:09<00:00, 110.04it/s]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "16.062878787878788\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fb9255a0438>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 167
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tLOIRNFmeE9C"
      },
      "source": [
        "# desafio, calcular para outra area"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QQu_9bAweMXS"
      },
      "source": [
        "resultados = []\n",
        "for i in tqdm(range(20)):\n",
        "  fator_sorte = i / 100\n",
        "  porcentagem_precisou_de_sorte = [simulacao_inteira(candidatos, corte_segunda_fase, vagas, fator_sorte) for i in range(1000)]\n",
        "  resultados.append([fator_sorte, np.mean(porcentagem_precisou_de_sorte)])\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kldNJXrhfjVu",
        "outputId": "ba6934fb-c898-4883-f2ea-0440c8261683",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        }
      },
      "source": [
        "\n",
        "df = pd.DataFrame(resultados, columns=[\"fator sorte\", \"% de pessoas que precisaram/nao tiveram sorte\"])\n",
        "plt.title(\"Medicina\")\n",
        "sns.lineplot(data=df, x=\"fator sorte\", y=\"% de pessoas que precisaram/nao tiveram sorte\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fb92518f748>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 170
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vbYe8_rhhOkc"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}